1 00:00:02,720 --> 00:00:10,560 Speaker 1: Bloomberg Audio Studios, Podcasts, radio news. You're listening to the 2 00:00:10,560 --> 00:00:14,520 Speaker 1: Bloomberg Intelligence Podcast. Catch us live weekdays at ten am 3 00:00:14,560 --> 00:00:17,799 Speaker 1: Eastern on Apple, Coarplay and Android Auto with the Bloomberg 4 00:00:17,880 --> 00:00:21,000 Speaker 1: Business App. Listen on demand wherever you get your podcasts, 5 00:00:21,320 --> 00:00:23,080 Speaker 1: or watch us live on YouTube. 6 00:00:23,600 --> 00:00:25,360 Speaker 2: We do want to keep you updated on what's happening 7 00:00:25,400 --> 00:00:28,159 Speaker 2: on the economic front end in Washington, DC, and for 8 00:00:28,200 --> 00:00:30,440 Speaker 2: that we go to Michael McKee, international economics and. 9 00:00:30,360 --> 00:00:31,960 Speaker 3: Policy correspondent who's in DC. 10 00:00:32,440 --> 00:00:36,080 Speaker 2: And the IMF just released its lowered forecast for world 11 00:00:36,080 --> 00:00:38,040 Speaker 2: growth this year and next, citing the risk of a 12 00:00:38,040 --> 00:00:41,040 Speaker 2: global trade war, also saying, Mike, the possibility of recession 13 00:00:41,040 --> 00:00:44,200 Speaker 2: in the US is rising to forty percent from twenty 14 00:00:44,200 --> 00:00:45,360 Speaker 2: seven percent in October. 15 00:00:45,400 --> 00:00:49,879 Speaker 4: What else did we learn, well, Alex, the world economic 16 00:00:49,880 --> 00:00:54,440 Speaker 4: outlook is rather cloudy. Indeed, according to the World Economic Outlook, basically, 17 00:00:54,680 --> 00:00:59,000 Speaker 4: the IMF had to retool all of their forecasts that 18 00:00:59,040 --> 00:01:03,280 Speaker 4: were underway when Donald Trump announced his tariffs on April second, 19 00:01:03,320 --> 00:01:06,120 Speaker 4: and so since then they've come up with a couple 20 00:01:06,120 --> 00:01:08,920 Speaker 4: of different scenarios because we don't know what exactly he's 21 00:01:08,959 --> 00:01:09,360 Speaker 4: going to do. 22 00:01:09,440 --> 00:01:11,720 Speaker 5: But the one they're using is one they call the 23 00:01:11,760 --> 00:01:16,560 Speaker 5: reference scenario, and it is that the global growth rate 24 00:01:16,640 --> 00:01:20,160 Speaker 5: will fall by half a percentage point this year, but 25 00:01:20,560 --> 00:01:23,880 Speaker 5: inflation won't rise as much because global slowdown will mean 26 00:01:23,920 --> 00:01:26,880 Speaker 5: that price pressures ease a little bit. That's for the 27 00:01:26,920 --> 00:01:30,600 Speaker 5: whole world. For the United States, it's much grimmer. We 28 00:01:30,680 --> 00:01:32,959 Speaker 5: will grow just one point five percent this year. That's 29 00:01:32,959 --> 00:01:36,720 Speaker 5: almost a full percentage point less than had been forecast 30 00:01:36,920 --> 00:01:41,240 Speaker 5: just two months ago by the IMF, and the inflation 31 00:01:41,360 --> 00:01:45,440 Speaker 5: rate will rise to three percent as unemployment rises above 32 00:01:45,560 --> 00:01:49,560 Speaker 5: four percent. So the US outlook is not particularly good 33 00:01:49,560 --> 00:01:52,520 Speaker 5: at all of this they blame on tariffs. 34 00:01:55,200 --> 00:01:57,360 Speaker 6: So Mike, that's kind of where I wanted to go here. 35 00:01:57,480 --> 00:02:00,000 Speaker 6: I mean, it does feel like a switch was thrown, 36 00:02:00,400 --> 00:02:02,240 Speaker 6: you know, three months ago, where we were in a 37 00:02:02,440 --> 00:02:06,520 Speaker 6: decent economic outlook GDP, you know, growing a three percent 38 00:02:06,600 --> 00:02:09,000 Speaker 6: range on a real basis, inflation probably still a little 39 00:02:09,040 --> 00:02:10,480 Speaker 6: higher than the Fed like it, but you know, two 40 00:02:10,480 --> 00:02:12,240 Speaker 6: and a half percent, stock. 41 00:02:12,000 --> 00:02:13,200 Speaker 7: Market at all time high. 42 00:02:14,000 --> 00:02:17,600 Speaker 6: Is are they ascribing, as you mentioned, the majority or 43 00:02:17,600 --> 00:02:21,200 Speaker 6: all of their cuts to this uncertainty surrounding tariffs. 44 00:02:22,200 --> 00:02:26,320 Speaker 5: Yes, and to modeling out the tariffs themselves and what 45 00:02:26,480 --> 00:02:28,600 Speaker 5: impact they might have of course they have to just 46 00:02:28,919 --> 00:02:32,120 Speaker 5: sort of pick a level of tariffs and try to 47 00:02:32,120 --> 00:02:34,480 Speaker 5: come up with an estimate for what that might mean, 48 00:02:34,520 --> 00:02:38,239 Speaker 5: since we don't have those numbers. But basically they're working 49 00:02:38,280 --> 00:02:42,799 Speaker 5: off what the administration had announced in early April, and 50 00:02:43,000 --> 00:02:50,000 Speaker 5: it's just an across the board drop in economic activity, GDP, markets, everything, 51 00:02:50,080 --> 00:02:52,840 Speaker 5: and of course, as you mentioned, they do put a 52 00:02:52,840 --> 00:02:55,440 Speaker 5: lot of weight not just on the financial costs but 53 00:02:55,560 --> 00:02:57,120 Speaker 5: on the costs of uncertainty. 54 00:03:00,000 --> 00:03:03,120 Speaker 2: Well, what areas through the IMF forecast or maybe the 55 00:03:03,240 --> 00:03:04,800 Speaker 2: least affected by all of this. 56 00:03:06,320 --> 00:03:11,359 Speaker 5: Interestingly, of the major economies, Great Britain is the least effective. 57 00:03:11,400 --> 00:03:15,440 Speaker 5: They don't see really any change in growth for the UK. 58 00:03:16,080 --> 00:03:18,960 Speaker 5: They forecast one point seven percent, that's down just the 59 00:03:19,040 --> 00:03:21,760 Speaker 5: tenth of a percent, and they don't see any change 60 00:03:21,800 --> 00:03:25,519 Speaker 5: in the UK inflation three point one percent or unemployment 61 00:03:25,560 --> 00:03:29,720 Speaker 5: four and a half percent, largely because the jump administration 62 00:03:30,200 --> 00:03:34,119 Speaker 5: didn't really put much in terms of tariffs on the UK, 63 00:03:34,320 --> 00:03:37,080 Speaker 5: just the ten percent universal tariffs, so they got off 64 00:03:37,120 --> 00:03:41,320 Speaker 5: a little bit easier than other countries. The Eurozone sees 65 00:03:41,400 --> 00:03:47,080 Speaker 5: a decline in growth of half a percent to just 66 00:03:47,240 --> 00:03:49,080 Speaker 5: seven tenths of a percent this year. 67 00:03:51,600 --> 00:03:52,240 Speaker 7: Hey, Mike did. 68 00:03:52,480 --> 00:03:54,880 Speaker 6: Does the IMF a pine at all on the duration 69 00:03:55,000 --> 00:03:56,839 Speaker 6: of tariffs? A lot of folks are saying, hey, if 70 00:03:56,920 --> 00:04:00,120 Speaker 6: you know President Trump backs off or waters down some 71 00:04:00,160 --> 00:04:03,440 Speaker 6: of these tariffs like he oftentimes does when push comes 72 00:04:03,480 --> 00:04:05,880 Speaker 6: to show where he that delays them that maybe the 73 00:04:05,920 --> 00:04:07,119 Speaker 6: impact won't be as bad. 74 00:04:07,200 --> 00:04:09,080 Speaker 7: Can you I MUS even model that out? 75 00:04:10,520 --> 00:04:12,520 Speaker 5: Well, they try in the sense that they're doing a 76 00:04:12,520 --> 00:04:15,040 Speaker 5: couple of different scenarios and they do a lighter tear 77 00:04:15,200 --> 00:04:20,239 Speaker 5: regime and so, which would mean that the impacts are lower. 78 00:04:20,279 --> 00:04:24,040 Speaker 5: There's still an impact, but they're lower than the reference forecast. 79 00:04:24,440 --> 00:04:27,240 Speaker 5: But they make no predictions about how likely any of 80 00:04:27,279 --> 00:04:28,279 Speaker 5: these are going to be. 81 00:04:31,240 --> 00:04:33,320 Speaker 2: When we take a look at other areas of the 82 00:04:33,320 --> 00:04:36,920 Speaker 2: global economy, what don't I m F say on China? 83 00:04:37,200 --> 00:04:40,480 Speaker 5: That's really interesting, Alex because the Chinese take a big 84 00:04:40,560 --> 00:04:43,640 Speaker 5: hit growth of just three point two percent, down one 85 00:04:43,680 --> 00:04:47,240 Speaker 5: point three percent from their January forecast. That would be 86 00:04:47,279 --> 00:04:51,240 Speaker 5: the lowest growth rate in China in decades. The Chinese 87 00:04:51,240 --> 00:04:54,599 Speaker 5: government is aiming at five percent this year, so it 88 00:04:54,680 --> 00:04:57,800 Speaker 5: could be a big hit to the Chinese. In terms 89 00:04:57,800 --> 00:04:59,920 Speaker 5: of inflation, they think. 90 00:04:59,760 --> 00:05:00,679 Speaker 8: They'll be flat. 91 00:05:01,360 --> 00:05:08,760 Speaker 5: That's maybe the best case scenario for China. In this situation, 92 00:05:09,080 --> 00:05:12,480 Speaker 5: a lot of people think they would experience disinflation, if 93 00:05:12,560 --> 00:05:13,320 Speaker 5: not deflation. 94 00:05:16,240 --> 00:05:18,960 Speaker 6: Mike, We're going to get some economic data this week 95 00:05:19,320 --> 00:05:21,760 Speaker 6: initial Joba's claims. I mean, what's the what are you 96 00:05:21,800 --> 00:05:24,520 Speaker 6: looking at this week to try to see those initial 97 00:05:24,600 --> 00:05:27,920 Speaker 6: signs of economic impact in what we like to call 98 00:05:27,960 --> 00:05:28,599 Speaker 6: the hard data. 99 00:05:29,960 --> 00:05:33,599 Speaker 5: Well, you'd be looking at things like jobless claims, which 100 00:05:33,640 --> 00:05:36,479 Speaker 5: haven't moved at all. They've actually come down some, so 101 00:05:36,520 --> 00:05:39,200 Speaker 5: it doesn't look like companies are letting people go. But 102 00:05:39,279 --> 00:05:42,360 Speaker 5: this is a kind of a different scenario because we're 103 00:05:42,400 --> 00:05:45,640 Speaker 5: in an environment coming out of the pandemic where companies 104 00:05:45,880 --> 00:05:49,159 Speaker 5: couldn't find workers, so they are more reluctant to let 105 00:05:49,200 --> 00:05:53,800 Speaker 5: people go, more reluctant to adjust in the face of 106 00:05:53,800 --> 00:05:57,320 Speaker 5: a potential recession than they might have been in the past. 107 00:05:57,440 --> 00:06:02,680 Speaker 5: Next week we'll get the the March jobs report and 108 00:06:03,040 --> 00:06:05,160 Speaker 5: the April jobs report rather, and that will give us 109 00:06:05,160 --> 00:06:08,359 Speaker 5: some indication in the unemployment and labor force numbers of 110 00:06:08,360 --> 00:06:12,960 Speaker 5: what's going on. If immigration, illegal or otherwise has basically 111 00:06:13,040 --> 00:06:15,800 Speaker 5: fallen to zero, the labor force won't grow, it should 112 00:06:15,800 --> 00:06:19,440 Speaker 5: probably shrink, and so that's something to keep an eye 113 00:06:19,440 --> 00:06:20,400 Speaker 5: on as well. 114 00:06:21,640 --> 00:06:22,159 Speaker 2: As well. 115 00:06:22,920 --> 00:06:24,320 Speaker 3: All right, Mike, super appreciated. 116 00:06:24,360 --> 00:06:26,960 Speaker 2: Down and you see there, Michael McKee, Boomerg International Economics 117 00:06:26,960 --> 00:06:28,800 Speaker 2: and Policy corresponding. 118 00:06:30,640 --> 00:06:34,359 Speaker 1: You're listening to the Bloomberg Intelligence Podcast. Catch us live 119 00:06:34,440 --> 00:06:37,520 Speaker 1: weekdays at ten am Eastern on Apple Corplay and Android 120 00:06:37,520 --> 00:06:40,840 Speaker 1: Auto with the Bloomberg Business App. Listen on demand wherever 121 00:06:40,920 --> 00:06:44,040 Speaker 1: you get your podcasts, or watch us live on YouTube. 122 00:06:44,839 --> 00:06:45,880 Speaker 7: Ox Steele, Paul Sweeney. 123 00:06:45,880 --> 00:06:48,839 Speaker 6: We're live here at New Jersey Institute of Technology. 124 00:06:49,400 --> 00:06:51,760 Speaker 7: That's NJ. It to the cool kids. I'm gonna stop 125 00:06:51,760 --> 00:06:53,520 Speaker 7: at the store and get some swag. I thic. 126 00:06:53,680 --> 00:06:55,200 Speaker 9: Okay, the whole team. 127 00:06:55,600 --> 00:06:57,480 Speaker 7: Oh no, I think I'm just me and maybe a 128 00:06:57,480 --> 00:06:58,960 Speaker 7: sticker for the car carsh man. 129 00:06:59,320 --> 00:07:02,000 Speaker 6: Yeah, we're hearing New New Jersey and some smart people 130 00:07:02,040 --> 00:07:03,919 Speaker 6: here doing some really smart research. 131 00:07:03,960 --> 00:07:04,720 Speaker 7: And we have one of the. 132 00:07:04,720 --> 00:07:09,320 Speaker 6: Next will be study Distinguished Professor Chemistry and Environmental Science 133 00:07:09,320 --> 00:07:12,200 Speaker 6: here and j T. When we talk to us about 134 00:07:12,240 --> 00:07:15,679 Speaker 6: what you're working on in terms of your research here, 135 00:07:15,880 --> 00:07:18,160 Speaker 6: I see the Biosmart Center. 136 00:07:18,240 --> 00:07:19,720 Speaker 7: That sounds pretty cool. What are you guys doing at 137 00:07:19,720 --> 00:07:20,920 Speaker 7: the bio Smart Center. 138 00:07:21,520 --> 00:07:24,760 Speaker 10: Thank you so much for having me at the Biosmass Center. 139 00:07:25,040 --> 00:07:29,560 Speaker 10: Our goal is to look for sustainable materials in terms 140 00:07:29,600 --> 00:07:33,960 Speaker 10: of chemistry to create technologies. 141 00:07:33,240 --> 00:07:34,560 Speaker 9: That will help people. 142 00:07:35,600 --> 00:07:40,520 Speaker 10: One of us technologies actually, you know, to detect pain. 143 00:07:41,840 --> 00:07:45,640 Speaker 9: Over one hundred US adults live. 144 00:07:45,560 --> 00:07:51,840 Speaker 10: With chronic pain and more than ten million individuals struggle 145 00:07:52,040 --> 00:07:55,520 Speaker 10: with prescription medications. But every time you go to the 146 00:07:55,600 --> 00:08:00,400 Speaker 10: hospital and the clinicians, physicians are required to measure pain. 147 00:08:01,200 --> 00:08:06,080 Speaker 10: And the only way we do that, despite advancement, is 148 00:08:06,240 --> 00:08:08,640 Speaker 10: to show you a facial scale. 149 00:08:09,760 --> 00:08:11,760 Speaker 3: They wear on the scale like what phases are you 150 00:08:11,880 --> 00:08:12,320 Speaker 3: right now? 151 00:08:12,520 --> 00:08:12,840 Speaker 7: Exact? 152 00:08:12,920 --> 00:08:15,280 Speaker 3: So what would your research be able to do? 153 00:08:16,640 --> 00:08:21,280 Speaker 10: So basically my research days, pain is biochemical in nature, 154 00:08:22,480 --> 00:08:25,720 Speaker 10: and when you have chronic pain, there's a lot of inflammation. 155 00:08:26,640 --> 00:08:29,920 Speaker 10: And when there's inflammation, there are chemicals that are about 156 00:08:30,040 --> 00:08:34,199 Speaker 10: chemicals that are produced by the body. By measuring be 157 00:08:34,200 --> 00:08:37,959 Speaker 10: faust of all, By knowing those biochemicals and measuring how 158 00:08:38,040 --> 00:08:40,880 Speaker 10: much they are, we can relate this to pain that 159 00:08:40,920 --> 00:08:44,800 Speaker 10: people are feeling. And so you won't need this subjective 160 00:08:44,840 --> 00:08:50,320 Speaker 10: approach to measure pain because if you have infants, for example, 161 00:08:50,360 --> 00:08:53,079 Speaker 10: if you have elderly, if you have people who are conscious, 162 00:08:53,200 --> 00:08:56,240 Speaker 10: they're not able to articulate their pain, and so you 163 00:08:56,280 --> 00:09:01,800 Speaker 10: can actually use about sensors a smart biosensors to give 164 00:09:01,840 --> 00:09:03,760 Speaker 10: you the level of pain that people are going through. 165 00:09:03,800 --> 00:09:06,240 Speaker 7: So where are you in terms of your research. 166 00:09:07,160 --> 00:09:09,400 Speaker 9: Our sensors that have been used currently? 167 00:09:10,120 --> 00:09:14,600 Speaker 10: Uh, you know, you know, we have collaborators in Upside 168 00:09:14,640 --> 00:09:19,679 Speaker 10: New York and they take human blood samples and they 169 00:09:19,760 --> 00:09:26,040 Speaker 10: measure the levels of molecules called cyclopgen is two or 170 00:09:26,080 --> 00:09:30,000 Speaker 10: inducible nitros oxide tastes, and. 171 00:09:30,400 --> 00:09:31,480 Speaker 9: They measure the level. 172 00:09:31,559 --> 00:09:36,080 Speaker 10: We combine this with artificial intelligence to be able to 173 00:09:36,520 --> 00:09:39,640 Speaker 10: give you the amount of pain that people are going through. 174 00:09:40,320 --> 00:09:43,600 Speaker 10: And for the most part, we've been able to link 175 00:09:44,280 --> 00:09:48,440 Speaker 10: the level that people suggest to the level that we're 176 00:09:48,440 --> 00:09:51,280 Speaker 10: measuring from our bios. 177 00:09:50,640 --> 00:09:53,360 Speaker 2: Are How far are we from like regular doctors and 178 00:09:53,440 --> 00:09:56,320 Speaker 2: nurses using it in hospitals currently? 179 00:09:56,400 --> 00:09:59,480 Speaker 10: I mean it's we've looked at close to one thousand 180 00:09:59,640 --> 00:10:04,280 Speaker 10: in the doors and we're getting eighty percent accuracy in 181 00:10:04,440 --> 00:10:07,280 Speaker 10: terms of what people tell you. At the end of 182 00:10:07,320 --> 00:10:11,640 Speaker 10: the day, pain is also individualistic, right, There are aspects 183 00:10:11,720 --> 00:10:15,599 Speaker 10: of pain that you know, you know, it depends on individuals. 184 00:10:15,600 --> 00:10:18,080 Speaker 9: You have pay tolerants, right exactly. 185 00:10:17,800 --> 00:10:19,960 Speaker 10: You know you have you know, you have saturation, you 186 00:10:20,040 --> 00:10:23,360 Speaker 10: can so there are so many other components that will impacted. 187 00:10:23,880 --> 00:10:26,400 Speaker 10: But in terms of being able to actually test this out, 188 00:10:26,520 --> 00:10:27,880 Speaker 10: we're doing this already. 189 00:10:28,320 --> 00:10:31,160 Speaker 6: So how does doing research at a place like njai 190 00:10:31,240 --> 00:10:33,120 Speaker 6: T How does that work? How do you balance like 191 00:10:33,240 --> 00:10:35,880 Speaker 6: I guess, research with teaching and all that, because I 192 00:10:35,920 --> 00:10:39,280 Speaker 6: know most professors have to deal with that across various disciplines. 193 00:10:40,040 --> 00:10:45,199 Speaker 10: In actual fact, there's correlation because in the classroom I 194 00:10:45,800 --> 00:10:49,800 Speaker 10: teach graduate students, I teach them the fundamentals, and then 195 00:10:49,840 --> 00:10:53,720 Speaker 10: we take it further from the classroom and actually do 196 00:10:53,800 --> 00:10:56,120 Speaker 10: this in the lab, and so there is a connection 197 00:10:56,280 --> 00:10:58,680 Speaker 10: between what you do in the classroom, what you're teaching 198 00:10:58,720 --> 00:11:00,800 Speaker 10: the classroom, and what you actually doing your love. 199 00:11:02,480 --> 00:11:06,000 Speaker 2: We talk a lot on Bloomberg here about tariff risks, 200 00:11:06,040 --> 00:11:07,640 Speaker 2: but economic risks. 201 00:11:07,920 --> 00:11:10,040 Speaker 3: About products being in short supply. 202 00:11:11,080 --> 00:11:13,560 Speaker 2: Is any of that relevant to the work that you do, Like, 203 00:11:13,640 --> 00:11:18,280 Speaker 2: are you worried about getting certain materials or products to fund. 204 00:11:18,080 --> 00:11:20,040 Speaker 3: And continue moving your research along? 205 00:11:21,120 --> 00:11:24,679 Speaker 10: Suddenly we're going to be affected because, as you know, 206 00:11:25,000 --> 00:11:28,520 Speaker 10: most research at the moment are funded by the federal government, 207 00:11:29,200 --> 00:11:34,800 Speaker 10: and so if there's less funding, there's less time that 208 00:11:34,920 --> 00:11:36,800 Speaker 10: we will not be able to support students to be 209 00:11:36,880 --> 00:11:40,319 Speaker 10: able to do the work, and so ultimately it will 210 00:11:40,360 --> 00:11:44,040 Speaker 10: impact our research, It would impact the classroom and what 211 00:11:44,160 --> 00:11:45,040 Speaker 10: we do. 212 00:11:45,160 --> 00:11:47,000 Speaker 7: What's the next step for you in your research? 213 00:11:47,200 --> 00:11:47,400 Speaker 11: Are you? 214 00:11:47,480 --> 00:11:51,480 Speaker 6: Are you working with a team other professors, maybe other universities. 215 00:11:52,040 --> 00:11:54,000 Speaker 7: What's your team looks like my. 216 00:11:54,000 --> 00:11:57,200 Speaker 10: Team at at the moment, we have six PhD students, 217 00:11:57,280 --> 00:12:01,640 Speaker 10: we have post dogs, we have clinicians that I'm working with, 218 00:12:01,679 --> 00:12:04,640 Speaker 10: those who have computer scientists who are looking at the 219 00:12:04,679 --> 00:12:09,000 Speaker 10: AI component of our work. So it's a whole center activity. 220 00:12:09,520 --> 00:12:12,199 Speaker 2: How did you come to research this particular part. I 221 00:12:12,200 --> 00:12:14,960 Speaker 2: always find that really fascinating when you like narrow it down, 222 00:12:15,040 --> 00:12:17,600 Speaker 2: like the field must be so broad, right, Like why 223 00:12:17,760 --> 00:12:18,559 Speaker 2: measuring pain? 224 00:12:19,679 --> 00:12:21,839 Speaker 10: That's a very good I'm sorry, that's a very good 225 00:12:21,920 --> 00:12:26,120 Speaker 10: question because I have always developed sensors for different things. 226 00:12:26,120 --> 00:12:29,640 Speaker 10: We developed sensors for the environment, We developed sensors to 227 00:12:29,720 --> 00:12:32,880 Speaker 10: measure different things. But I had a friend whose daughter 228 00:12:33,559 --> 00:12:38,839 Speaker 10: was suffering from sickle cell and you know, and she asks, 229 00:12:39,040 --> 00:12:39,760 Speaker 10: you know a view. 230 00:12:40,320 --> 00:12:42,840 Speaker 9: You know, many times she's in crisis. 231 00:12:43,600 --> 00:12:46,959 Speaker 10: Physicians that tificately they cannot really assess whether or not 232 00:12:47,080 --> 00:12:49,600 Speaker 10: she's in pain. And I thought, well, that should be 233 00:12:49,640 --> 00:12:53,199 Speaker 10: easy as long as we can find a particular molecule, 234 00:12:53,720 --> 00:12:54,800 Speaker 10: we can measure that. 235 00:12:55,200 --> 00:12:56,800 Speaker 9: And I thought somebody should have done that. 236 00:12:58,440 --> 00:13:01,280 Speaker 3: It seems so obvious now I'm getting But we did. 237 00:13:01,360 --> 00:13:03,720 Speaker 10: We looked in literature and we realize it's actually not. 238 00:13:04,400 --> 00:13:08,000 Speaker 10: And this is where we started the work fifteen years ago. 239 00:13:08,160 --> 00:13:11,439 Speaker 6: And our doctor's clinicians in the marketplace, are they receptive 240 00:13:11,520 --> 00:13:15,240 Speaker 6: to your research and in what you're in your products? 241 00:13:15,320 --> 00:13:16,560 Speaker 7: Clinicians are receptive. 242 00:13:16,679 --> 00:13:18,559 Speaker 9: But my own daughter is not a sociologist. 243 00:13:19,000 --> 00:13:23,280 Speaker 10: She's a physician and she's at Opkins, and she says, Mommy, 244 00:13:23,360 --> 00:13:27,360 Speaker 10: if we can find an instrument that would tell us 245 00:13:27,360 --> 00:13:29,400 Speaker 10: how much pain people are in, this is going to 246 00:13:29,480 --> 00:13:33,160 Speaker 10: be significant because we get people, a lot of people coming, 247 00:13:33,840 --> 00:13:35,440 Speaker 10: they say they're in pain. 248 00:13:35,559 --> 00:13:38,959 Speaker 7: We're required to treat the pain. 249 00:13:39,000 --> 00:13:42,000 Speaker 10: But how can we actually assess how much pain they're in? 250 00:13:42,559 --> 00:13:44,480 Speaker 10: So if somebody says some ten out of ten, who 251 00:13:44,480 --> 00:13:45,319 Speaker 10: are you to say they're not? 252 00:13:45,640 --> 00:13:45,720 Speaker 2: Right? 253 00:13:46,440 --> 00:13:46,640 Speaker 8: Yep? 254 00:13:46,760 --> 00:13:49,960 Speaker 2: Interesting, well, really great stuff. Congratulations on all of it. 255 00:13:49,960 --> 00:13:52,560 Speaker 2: We wish you a lot of luck. It seems like 256 00:13:52,559 --> 00:13:55,800 Speaker 2: an amazing, amazing research that you guys wind up doing here. 257 00:13:55,840 --> 00:13:57,040 Speaker 3: Thank you so very much. 258 00:13:57,679 --> 00:14:01,719 Speaker 2: That is a professor Wami joining us a NJ T 259 00:14:01,880 --> 00:14:06,240 Speaker 2: Distinguished Professor of Chemistry and Environmental Science, on measuring pain. 260 00:14:06,360 --> 00:14:08,040 Speaker 7: I never thought about it. I thought it was again just. 261 00:14:08,000 --> 00:14:10,480 Speaker 3: A little But the inflammation thing, that's so key. I 262 00:14:10,480 --> 00:14:12,160 Speaker 3: feel like everything wrong with us is inflammation. 263 00:14:12,160 --> 00:14:14,199 Speaker 7: Really, she's nodding. 264 00:14:14,240 --> 00:14:16,720 Speaker 3: Then I said that, so therefore it must be real exactly. 265 00:14:18,480 --> 00:14:22,160 Speaker 1: You're listening to the Bloomberg Intelligence Podcast. Catch us live 266 00:14:22,240 --> 00:14:25,320 Speaker 1: weekdays at ten am Eastern on Apple, Coarplay, and Android 267 00:14:25,360 --> 00:14:28,640 Speaker 1: Auto with the Bloomberg Business App. Listen on demand wherever 268 00:14:28,720 --> 00:14:31,840 Speaker 1: you get your podcasts, or watch us live on YouTube. 269 00:14:32,600 --> 00:14:35,360 Speaker 6: All right, let's talk to our next guest here, Tara Alvarez, 270 00:14:35,680 --> 00:14:41,840 Speaker 6: nj T Distinguished Professor Biomedical Engineering, talking about treating vision disorders. 271 00:14:41,840 --> 00:14:44,280 Speaker 6: I've had a vision disorder since sixth grade. I've had glasses. 272 00:14:44,800 --> 00:14:46,160 Speaker 3: So is that a vision disorders? 273 00:14:46,200 --> 00:14:47,880 Speaker 7: Is just I don't know, maybe it's just bad eyes. 274 00:14:47,920 --> 00:14:49,240 Speaker 3: That is there a distinction? 275 00:14:49,360 --> 00:14:50,560 Speaker 9: I don't here's a distinction. 276 00:14:50,680 --> 00:14:52,640 Speaker 6: Tarah, thank you so much for joining us here at 277 00:14:52,720 --> 00:14:55,440 Speaker 6: nj T your home. Talk to us about the work 278 00:14:55,480 --> 00:14:58,560 Speaker 6: you're doing. What are you looking at? What's the vision 279 00:14:58,560 --> 00:15:00,000 Speaker 6: disorders that you guys are looking at. 280 00:15:00,280 --> 00:15:03,920 Speaker 11: Thanks so much for having me, and you're right glasses 281 00:15:04,040 --> 00:15:06,160 Speaker 11: is what most people think of when they think about 282 00:15:06,160 --> 00:15:09,960 Speaker 11: an eye disorder, and if you can imagine, it's very 283 00:15:10,000 --> 00:15:13,720 Speaker 11: difficult to know what clear vision looks like unless you've 284 00:15:13,720 --> 00:15:18,000 Speaker 11: been fitted for your first pair of glasses. My expertise 285 00:15:18,200 --> 00:15:21,440 Speaker 11: is in how the brain brings visual information into the brain, 286 00:15:21,800 --> 00:15:24,000 Speaker 11: which is the idea of using the eyes as a 287 00:15:24,040 --> 00:15:27,720 Speaker 11: team to get the information into the brain. And if 288 00:15:27,760 --> 00:15:30,600 Speaker 11: you don't do that well, you might not even realize 289 00:15:30,640 --> 00:15:34,080 Speaker 11: you have it, but it can result in problems when 290 00:15:34,080 --> 00:15:36,840 Speaker 11: doing near work such as reading, working on your phone, 291 00:15:37,200 --> 00:15:41,760 Speaker 11: working on computers, and vision therapy works quite well for 292 00:15:41,880 --> 00:15:46,000 Speaker 11: this condition known as convergence insufficiency, which is the inability 293 00:15:46,040 --> 00:15:50,040 Speaker 11: of the eyes to work well as a team. 294 00:15:49,160 --> 00:15:52,320 Speaker 3: How do you have how do you fix that? I 295 00:15:52,400 --> 00:15:53,920 Speaker 3: guess or how do you find it? 296 00:15:53,960 --> 00:15:54,960 Speaker 9: And then how do you fix it? 297 00:15:55,120 --> 00:15:59,080 Speaker 11: Great questions. So vision therapy, which is basically like a 298 00:15:59,120 --> 00:16:03,040 Speaker 11: form of physical or occupational therapy for your eyes, strengthens 299 00:16:03,120 --> 00:16:06,400 Speaker 11: the eye muscles and the communication between the brain and 300 00:16:06,600 --> 00:16:10,040 Speaker 11: the eyes. My work has been funded mostly through the 301 00:16:10,160 --> 00:16:13,680 Speaker 11: National Institutes of Health, which is very critical in funding 302 00:16:14,040 --> 00:16:18,000 Speaker 11: research that has direct impact to our society. You can 303 00:16:18,040 --> 00:16:21,040 Speaker 11: find this by going to an eye doctor, so an 304 00:16:21,080 --> 00:16:24,560 Speaker 11: optometrist or an ophthalmologist, and they can do an exam, 305 00:16:24,880 --> 00:16:27,240 Speaker 11: but most people don't even know that they have it, 306 00:16:27,320 --> 00:16:29,960 Speaker 11: so they don't even realize that this is a problem. 307 00:16:30,080 --> 00:16:33,480 Speaker 11: So typical problems people can have as they get headaches 308 00:16:33,480 --> 00:16:36,520 Speaker 11: while reading, they feel like they read slowly, they get 309 00:16:36,560 --> 00:16:39,600 Speaker 11: blurry vision, double vision, and it takes them much longer. 310 00:16:39,680 --> 00:16:42,760 Speaker 11: So it's not that they have a cognitive or a 311 00:16:43,240 --> 00:16:46,160 Speaker 11: problem in learning, it's that they're struggling to get the 312 00:16:46,240 --> 00:16:48,200 Speaker 11: visual information into the brain. 313 00:16:48,600 --> 00:16:52,600 Speaker 7: How common is this affliction or this issue. 314 00:16:52,200 --> 00:16:55,080 Speaker 11: So depending on how you do, the diagnosis is present 315 00:16:55,160 --> 00:16:58,120 Speaker 11: in between four and twelve percent, so you can say 316 00:16:58,160 --> 00:17:00,600 Speaker 11: roughly eight percent of the population. 317 00:17:02,160 --> 00:17:05,040 Speaker 2: You mentioned the funding. What's your level of confidence that 318 00:17:05,080 --> 00:17:07,280 Speaker 2: funding for this kind of study will stay. 319 00:17:08,600 --> 00:17:12,880 Speaker 11: I'm unclear right now. So right now we have I'm 320 00:17:12,880 --> 00:17:17,000 Speaker 11: on my second randomized clinical trial where we're concentrating on 321 00:17:17,400 --> 00:17:23,040 Speaker 11: concussions because we have the CDC released in December of 322 00:17:23,080 --> 00:17:27,240 Speaker 11: twenty four that concussion costs is about forty billion dollars 323 00:17:27,320 --> 00:17:31,359 Speaker 11: a year. And if you have had a concussion, especially 324 00:17:31,440 --> 00:17:36,400 Speaker 11: multiple concussions, you can develop persistent postconcussive symptoms and out 325 00:17:36,440 --> 00:17:40,240 Speaker 11: of that population, about half of them have this convergence 326 00:17:40,280 --> 00:17:43,240 Speaker 11: and sufficiency, which is that teeming problem of the eyes. 327 00:17:43,960 --> 00:17:48,000 Speaker 11: So it is quite common. It's very impactful. My program 328 00:17:48,000 --> 00:17:51,080 Speaker 11: officer at the National Eye Institute within the National Institutes 329 00:17:51,160 --> 00:17:55,199 Speaker 11: of Health is extremely excited about our work, and in 330 00:17:55,240 --> 00:17:57,840 Speaker 11: the past administration, I would have much more confidence that 331 00:17:57,880 --> 00:18:01,120 Speaker 11: we would have funding to continue. That is very important work, 332 00:18:01,160 --> 00:18:03,320 Speaker 11: but it is something I have a lot of concerns 333 00:18:03,320 --> 00:18:04,040 Speaker 11: about right now. 334 00:18:05,000 --> 00:18:07,159 Speaker 6: How often do you get funded or how often do 335 00:18:07,280 --> 00:18:10,760 Speaker 6: most researchers get fund Is this an annual thing? 336 00:18:11,320 --> 00:18:15,080 Speaker 11: So typically you get what's called an R one, which 337 00:18:15,119 --> 00:18:19,240 Speaker 11: is five years of funding, and you are reviewed every year, okay, 338 00:18:19,720 --> 00:18:22,600 Speaker 11: and typically with a randomized clinical trial, which is what 339 00:18:22,760 --> 00:18:28,120 Speaker 11: I'm leading. That's done in collaboration with Children's Hospital Philadelphia 340 00:18:28,240 --> 00:18:33,040 Speaker 11: as well as Rutgers chop Yes and Rutgers University. It 341 00:18:33,080 --> 00:18:36,040 Speaker 11: takes time because this is a rehabilitation and it's a 342 00:18:36,080 --> 00:18:40,120 Speaker 11: longitudinal study, and it's also done with Saless University of Drexel, 343 00:18:40,800 --> 00:18:45,120 Speaker 11: so it's not something that happens overnight. It takes time 344 00:18:45,160 --> 00:18:48,760 Speaker 11: to acquire this data. But it's really critical because the 345 00:18:48,840 --> 00:18:51,680 Speaker 11: knowledge that I'm gaining from this study has been patented 346 00:18:52,280 --> 00:18:54,919 Speaker 11: where NNGIT holds the patents, and that led to our 347 00:18:54,960 --> 00:18:59,040 Speaker 11: startup company, Ocular Motor Technologies. And the key reason I 348 00:18:59,119 --> 00:19:01,760 Speaker 11: became a biomedical engineer is I want to have a 349 00:19:01,800 --> 00:19:06,960 Speaker 11: positive impact on others, specifically in the healthcare sector. And 350 00:19:07,000 --> 00:19:11,800 Speaker 11: it's my children that actually inspired the core technology of 351 00:19:11,840 --> 00:19:14,880 Speaker 11: our company, which is the idea of trying to do 352 00:19:14,960 --> 00:19:18,879 Speaker 11: the therapy that works very well but is incredibly boring. So 353 00:19:18,920 --> 00:19:21,520 Speaker 11: if you can put the therapy in a virtual reality 354 00:19:21,560 --> 00:19:24,520 Speaker 11: headset and make it into a game. If you have 355 00:19:24,640 --> 00:19:27,679 Speaker 11: a child, a mine or almost all grown now, but 356 00:19:28,040 --> 00:19:30,159 Speaker 11: it's not difficult to get a kid to play a 357 00:19:30,240 --> 00:19:32,919 Speaker 11: VR game. And in essence, we are sugar coating the 358 00:19:32,920 --> 00:19:36,320 Speaker 11: therapy and they think they're having fun, but in actuality 359 00:19:36,400 --> 00:19:39,280 Speaker 11: it's sugar coating a ton of science to get those 360 00:19:39,359 --> 00:19:41,119 Speaker 11: eyes to work better together. 361 00:19:41,400 --> 00:19:44,200 Speaker 3: It's like when I put kale in the oven. 362 00:19:44,600 --> 00:19:44,919 Speaker 8: Correct. 363 00:19:45,080 --> 00:19:48,520 Speaker 2: Yeah, it's a lot of the sad lines exactly. So 364 00:19:49,040 --> 00:19:52,440 Speaker 2: what is the exit strategy for the startup and can 365 00:19:52,440 --> 00:19:54,359 Speaker 2: you get outside funding at the same time. 366 00:19:54,800 --> 00:19:58,840 Speaker 11: So we have been funded through the NSF through SBIR, 367 00:19:59,000 --> 00:20:02,240 Speaker 11: which is the small Bestiness Investigator grants. We've had both 368 00:20:02,280 --> 00:20:05,760 Speaker 11: phase one and phase two, and we also participated in 369 00:20:05,840 --> 00:20:11,320 Speaker 11: an NNGIT iCore program, and we did a national version 370 00:20:11,320 --> 00:20:15,960 Speaker 11: of iCore, which is basically teaching professors how to create 371 00:20:16,040 --> 00:20:19,520 Speaker 11: and translate their science out of the lab and to 372 00:20:19,600 --> 00:20:20,760 Speaker 11: have a positive impact. 373 00:20:21,520 --> 00:20:22,000 Speaker 9: Amazing. 374 00:20:22,040 --> 00:20:23,399 Speaker 2: We have to leave it there. I'm sorry, we're up 375 00:20:23,400 --> 00:20:25,880 Speaker 2: against the clock. Listen, don't leave me yet quite yet. 376 00:20:26,119 --> 00:20:28,720 Speaker 2: Thank you so much. We really appreciate Tara Tara Alvarez 377 00:20:28,800 --> 00:20:32,840 Speaker 2: NJIT Distinguished Professor Biomedical Engineering joining us here at NJIT. 378 00:20:33,040 --> 00:20:35,760 Speaker 2: I love hearing about all the variety of work. It 379 00:20:35,880 --> 00:20:37,840 Speaker 2: is truly truly amazing. 380 00:20:39,560 --> 00:20:43,240 Speaker 1: You're listening to the Bloomberg Intelligence Podcast. Catch us live 381 00:20:43,320 --> 00:20:46,720 Speaker 1: weekdays at ten am Eastern on Applecarclay and Android Auto 382 00:20:46,840 --> 00:20:49,879 Speaker 1: with the Bloomberg Business App. Listen on demand wherever you 383 00:20:49,920 --> 00:20:52,920 Speaker 1: get your podcasts, or watch us live on YouTube. 384 00:20:53,680 --> 00:20:55,359 Speaker 6: Right now, when we get back to some of our 385 00:20:55,359 --> 00:20:59,800 Speaker 6: speakers here at NJIT, Chow Yohon and J T alumnus 386 00:21:00,119 --> 00:21:03,080 Speaker 6: and he's co foundered CEO Princeton New Energy, which is 387 00:21:03,080 --> 00:21:07,960 Speaker 6: a global leader in lithium ion battery direct recycling. I 388 00:21:08,040 --> 00:21:10,600 Speaker 6: know that's what I learned from my he's now twenty 389 00:21:10,640 --> 00:21:13,320 Speaker 6: nine year old engineer son back when he's like twelve. 390 00:21:13,400 --> 00:21:15,800 Speaker 6: He explained to me these lithum ion batteries that we're 391 00:21:15,800 --> 00:21:18,600 Speaker 6: powering his little remale control cars, how serious you have 392 00:21:18,640 --> 00:21:21,120 Speaker 6: to handle them? You can't just throw them away. And 393 00:21:21,160 --> 00:21:23,919 Speaker 6: here's twelve and he's schooling me there, So I know 394 00:21:24,000 --> 00:21:26,080 Speaker 6: this thing. Child talked to us about your company. What 395 00:21:26,080 --> 00:21:28,320 Speaker 6: are you guys trying to do here? Because these batteries 396 00:21:28,359 --> 00:21:29,160 Speaker 6: are everywhere now. 397 00:21:29,880 --> 00:21:33,000 Speaker 12: Yeah, So Princeton New Energy, we have a great technology 398 00:21:33,040 --> 00:21:36,159 Speaker 12: in the use in plasma to recycle lithim ion battery 399 00:21:36,680 --> 00:21:39,600 Speaker 12: is much lower cost roughly forty to fifty percent lower 400 00:21:39,880 --> 00:21:44,400 Speaker 12: than the traditional recycling technology and also much more cleaner 401 00:21:44,560 --> 00:21:47,879 Speaker 12: compared with the traditional lead as the leaching process. So 402 00:21:47,960 --> 00:21:52,240 Speaker 12: that's why recycling technology we need in the US is 403 00:21:52,280 --> 00:21:55,200 Speaker 12: a cleaner and a cheaper So talk about the supply 404 00:21:55,320 --> 00:21:58,000 Speaker 12: chain for the battery. The biggest a problem for the 405 00:21:58,080 --> 00:22:01,280 Speaker 12: US right now is that the is still too expensive. 406 00:22:01,800 --> 00:22:04,480 Speaker 12: So how we can reduce the cost for the EV 407 00:22:04,720 --> 00:22:07,720 Speaker 12: is important. So there's a more than half of the 408 00:22:07,800 --> 00:22:10,119 Speaker 12: costs inside the battery, which is a they call the 409 00:22:10,160 --> 00:22:14,919 Speaker 12: cathode active materials. So the direct recycling our technology is 410 00:22:14,960 --> 00:22:19,080 Speaker 12: to direct extract those cathode active materials outside from the 411 00:22:19,160 --> 00:22:22,440 Speaker 12: old batteries that you can reuse. And at the same time, 412 00:22:22,520 --> 00:22:24,879 Speaker 12: we do not want to produce a lot of waste. 413 00:22:25,119 --> 00:22:28,600 Speaker 12: So in the traditional way using the software acid, you're 414 00:22:28,680 --> 00:22:31,320 Speaker 12: leaching all the metals and know at the end you 415 00:22:31,359 --> 00:22:34,359 Speaker 12: get a lot of sodium software and we don't have 416 00:22:34,480 --> 00:22:36,960 Speaker 12: the place to dumb them right now, So that's why 417 00:22:37,000 --> 00:22:39,399 Speaker 12: we need great technology to do that and which is 418 00:22:39,440 --> 00:22:41,760 Speaker 12: a much lower cost. So that's what we're doing. 419 00:22:41,960 --> 00:22:43,840 Speaker 3: So let's go to the cathode part first. 420 00:22:43,840 --> 00:22:49,520 Speaker 2: So you're doing that forty percent cheaper than competitors how so, Yeah. 421 00:22:49,520 --> 00:22:52,359 Speaker 12: Because of the traditional way, you need to break the 422 00:22:52,400 --> 00:22:56,399 Speaker 12: old batteries to down to the element. So using the acid, 423 00:22:56,840 --> 00:22:59,760 Speaker 12: so we don't destroy the cathode materials which is a 424 00:23:00,680 --> 00:23:03,280 Speaker 12: fix them reuse them. So that's how we reduce the 425 00:23:03,400 --> 00:23:05,840 Speaker 12: cost and using our plasma technology. 426 00:23:06,720 --> 00:23:11,240 Speaker 6: So where are we with just battery technology and recycling, 427 00:23:11,280 --> 00:23:13,480 Speaker 6: I mean, are there more advances to go here? 428 00:23:13,800 --> 00:23:14,800 Speaker 7: Because it feels like. 429 00:23:15,760 --> 00:23:18,879 Speaker 6: That's such a key part of electric vehicles, just electric 430 00:23:19,240 --> 00:23:20,080 Speaker 6: power going forward. 431 00:23:20,640 --> 00:23:24,800 Speaker 12: Yeah, so it's not only for the EVA, but also 432 00:23:24,920 --> 00:23:29,359 Speaker 12: like the Andy storage batteries. Yes, as the big the 433 00:23:29,440 --> 00:23:33,600 Speaker 12: storage system, so traditional technology we're twined to build in 434 00:23:33,640 --> 00:23:37,240 Speaker 12: the US, but it's very expensive and the processing costs 435 00:23:37,280 --> 00:23:39,960 Speaker 12: is also very expensive. So that's why in the US 436 00:23:40,000 --> 00:23:42,880 Speaker 12: we're trying to scaleing up our technology. So the company 437 00:23:42,960 --> 00:23:47,359 Speaker 12: was founded in twenty nineteen and we have technology and 438 00:23:47,480 --> 00:23:50,040 Speaker 12: after that we have the large space lab in New 439 00:23:50,119 --> 00:23:52,760 Speaker 12: Jersey which is a close to Princeton. And also we 440 00:23:52,840 --> 00:23:55,760 Speaker 12: have a build up pilot production line which is about 441 00:23:55,920 --> 00:23:58,120 Speaker 12: three four years ago right now it is upruning about 442 00:23:58,160 --> 00:24:02,320 Speaker 12: two years which is in Dallas, Texas, and starting from 443 00:24:02,400 --> 00:24:05,080 Speaker 12: last year, we are building the first commercial scale of 444 00:24:05,080 --> 00:24:08,320 Speaker 12: the production line in South Kara and Chester County. So 445 00:24:08,359 --> 00:24:10,760 Speaker 12: in this one we are able to recycle five thousand 446 00:24:10,840 --> 00:24:13,560 Speaker 12: towns as a face one and we target to expand 447 00:24:13,600 --> 00:24:16,720 Speaker 12: to thirty thousand towns end to recycle the batteries. 448 00:24:16,760 --> 00:24:18,920 Speaker 2: Do you have to have end buyers that will contract 449 00:24:18,960 --> 00:24:21,280 Speaker 2: that material for you to feel confident putting in that 450 00:24:21,359 --> 00:24:22,680 Speaker 2: kind of capex. 451 00:24:22,760 --> 00:24:25,200 Speaker 12: Yes, we need that and do you have that? We 452 00:24:25,600 --> 00:24:28,360 Speaker 12: do have the feed stock provider which give us the 453 00:24:28,400 --> 00:24:31,720 Speaker 12: waste batteries and it were coming from like a cell 454 00:24:31,840 --> 00:24:35,720 Speaker 12: manufacturers who make the batteries. They are manufacturing scrap, so 455 00:24:35,760 --> 00:24:38,120 Speaker 12: we do have a contract with them to recycle their 456 00:24:40,040 --> 00:24:43,600 Speaker 12: manufacturing scrap. We do have a contract with auto ems 457 00:24:43,680 --> 00:24:47,439 Speaker 12: and also the Junkyard players who have a lot of 458 00:24:47,480 --> 00:24:50,040 Speaker 12: waste batteries, so we also have a contract for that. 459 00:24:50,040 --> 00:24:52,200 Speaker 3: One who's buying them though, so. 460 00:24:52,160 --> 00:24:55,920 Speaker 12: Currently we are selling to the leaching companies who need 461 00:24:55,960 --> 00:24:59,720 Speaker 12: those batteries to continue to get medals for the later usage. 462 00:25:00,320 --> 00:25:02,399 Speaker 6: How are you funding your company? I'm a former banker, 463 00:25:02,400 --> 00:25:04,439 Speaker 6: so I always think about the money. How are you 464 00:25:04,440 --> 00:25:05,280 Speaker 6: funding this company? 465 00:25:05,480 --> 00:25:08,680 Speaker 12: That's a very important part. So we close the two 466 00:25:08,760 --> 00:25:12,080 Speaker 12: rounds of the investment. We got CIZ round and a RAND. 467 00:25:12,280 --> 00:25:15,719 Speaker 12: So we have a private investors who interest with US 468 00:25:15,800 --> 00:25:19,520 Speaker 12: investor US and supporting us, and those the investors some 469 00:25:19,640 --> 00:25:23,760 Speaker 12: of the finishing investors AMOWT Strategy Investor so. And on 470 00:25:23,800 --> 00:25:25,960 Speaker 12: top of this, we get a big support from the 471 00:25:25,960 --> 00:25:29,439 Speaker 12: Department Energy in the past six years, starting from like 472 00:25:29,480 --> 00:25:32,639 Speaker 12: a smaller grand SBR later on we have a larger grant, 473 00:25:33,080 --> 00:25:35,840 Speaker 12: so we got rough about twenty million dollars pouring us. 474 00:25:36,600 --> 00:25:39,199 Speaker 3: Well, what is your level of confidence that that continues. 475 00:25:40,440 --> 00:25:44,159 Speaker 12: I think for the United States, critical minerals are very important, 476 00:25:44,680 --> 00:25:46,919 Speaker 12: So we don't have so many minds in the US. 477 00:25:47,800 --> 00:25:51,560 Speaker 12: What we need is how we can leverage those waste 478 00:25:51,640 --> 00:25:54,000 Speaker 12: stuff and how to reuse them. So that's why I 479 00:25:54,000 --> 00:25:58,240 Speaker 12: think recycling technology is a critical for US to secure 480 00:25:58,240 --> 00:26:02,960 Speaker 12: the critical minerals and will link to the US energy security. 481 00:26:03,080 --> 00:26:06,960 Speaker 12: So I think for our technology is very critical for 482 00:26:07,000 --> 00:26:10,800 Speaker 12: the United States for the materials what we need and 483 00:26:10,880 --> 00:26:13,480 Speaker 12: also for the batteries what we're going to build. So 484 00:26:13,520 --> 00:26:16,440 Speaker 12: that's what we need, and just give you a little 485 00:26:16,480 --> 00:26:20,600 Speaker 12: bit numbers. So currently US don't produce any catle the materials, 486 00:26:21,119 --> 00:26:24,600 Speaker 12: so all the materials we import from outside. So directly 487 00:26:24,680 --> 00:26:28,399 Speaker 12: cycling we use the waste batteries and produce the catle 488 00:26:28,440 --> 00:26:32,160 Speaker 12: the materials to make new batteries. And that's content more 489 00:26:32,160 --> 00:26:35,240 Speaker 12: than half of the value inside the little IONN batteries. 490 00:26:35,520 --> 00:26:39,000 Speaker 12: So how important is That's why we believe the grant 491 00:26:39,040 --> 00:26:43,160 Speaker 12: will continue to support this critical minerals research and also 492 00:26:43,240 --> 00:26:45,040 Speaker 12: the support our energy security. 493 00:26:45,400 --> 00:26:47,679 Speaker 7: So you get your masters and your PhD here. 494 00:26:47,600 --> 00:26:49,880 Speaker 12: Right, that's right in chemistry department. 495 00:26:49,960 --> 00:26:53,200 Speaker 7: That sounds fun. How was your experience here? 496 00:26:53,760 --> 00:26:59,280 Speaker 12: It's awesome. I really enjoyed the research here. So basically 497 00:26:59,640 --> 00:27:04,280 Speaker 12: it's my very strong the research and the Engineering foundation. 498 00:27:04,880 --> 00:27:07,520 Speaker 12: So I think that's would be very critical because once 499 00:27:07,560 --> 00:27:11,760 Speaker 12: you're move into the next step, so doing research basically 500 00:27:11,920 --> 00:27:13,800 Speaker 12: finished PC, no one's going to teach you how to 501 00:27:13,840 --> 00:27:17,760 Speaker 12: do it. You have very strong the experience, how to 502 00:27:17,800 --> 00:27:22,360 Speaker 12: design your research, how to set up everything, and then 503 00:27:22,720 --> 00:27:25,520 Speaker 12: after research, how to write a paper and the publications 504 00:27:25,840 --> 00:27:29,800 Speaker 12: and more important, how to find the research topics, write 505 00:27:29,800 --> 00:27:32,520 Speaker 12: the proposals to get a grant. So yeah, we got 506 00:27:32,520 --> 00:27:34,560 Speaker 12: I got a pretty good foundation here. 507 00:27:34,760 --> 00:27:38,639 Speaker 6: Very good good advertisement for walking advertisement. 508 00:27:38,240 --> 00:27:39,320 Speaker 7: Or Ji t for sure. 509 00:27:39,400 --> 00:27:43,720 Speaker 6: Chalian, co founder and CEO Princeton New Energy talking about 510 00:27:44,200 --> 00:27:47,600 Speaker 6: recycling those batteries which are in the cars and a 511 00:27:47,640 --> 00:27:48,639 Speaker 6: lot of other places too. 512 00:27:48,800 --> 00:27:49,560 Speaker 3: That's really amazing. 513 00:27:50,480 --> 00:27:52,160 Speaker 2: I'm just interested to see and we've heard from many 514 00:27:52,640 --> 00:27:56,439 Speaker 2: professors here as well, obviously with the startup about funding 515 00:27:56,480 --> 00:27:59,399 Speaker 2: and how important government funding is and how uncertain that 516 00:27:59,440 --> 00:28:02,720 Speaker 2: path to government funding is as well. 517 00:28:02,880 --> 00:28:06,600 Speaker 1: You're listening to the Bloomberg Intelligence Podcast. Catch us live 518 00:28:06,680 --> 00:28:09,760 Speaker 1: weekdays at ten am Eastern on Apple, Cocklay and Android 519 00:28:09,800 --> 00:28:13,080 Speaker 1: Auto with the Bloomberg Business app. Listen on demand wherever 520 00:28:13,160 --> 00:28:16,280 Speaker 1: you get your podcasts, or watch us live on YouTube. 521 00:28:16,880 --> 00:28:20,280 Speaker 3: I'le steal here alongside Paul Sweeney. This is Bloomberg Intelligence. 522 00:28:19,840 --> 00:28:23,120 Speaker 2: Radio Broadcasting two live from Newark, New Jersey at JIT 523 00:28:23,320 --> 00:28:26,520 Speaker 2: New Jersey Institute of Technology, where they envroll thirteen thousand 524 00:28:26,520 --> 00:28:29,320 Speaker 2: students and are really some of the leaders in technology 525 00:28:29,840 --> 00:28:32,639 Speaker 2: and science within the country. Joining us now here is 526 00:28:32,760 --> 00:28:37,240 Speaker 2: Eric Fortune and JIT Associate Professor of Biological Sciences. 527 00:28:37,640 --> 00:28:40,120 Speaker 3: He recently led this really cool. He recently led. 528 00:28:39,920 --> 00:28:44,240 Speaker 2: A team competing to record the biodiversity in a square 529 00:28:44,320 --> 00:28:47,480 Speaker 2: kilometer of the Amazon rainforest. 530 00:28:47,560 --> 00:28:49,560 Speaker 3: And this is after six year completion. 531 00:28:49,720 --> 00:28:53,080 Speaker 2: Your team walked away with a five million dollars prize. 532 00:28:53,120 --> 00:28:55,560 Speaker 2: This is really exciting. Can you walk us through what 533 00:28:55,600 --> 00:28:57,560 Speaker 2: that was like and how you did it and all 534 00:28:57,560 --> 00:28:58,240 Speaker 2: that fun stuff. 535 00:28:58,520 --> 00:29:03,000 Speaker 8: Well, it's a super exciting project that we were part of. 536 00:29:03,480 --> 00:29:07,000 Speaker 8: It was sponsored by this group called the X Prize, 537 00:29:07,080 --> 00:29:14,000 Speaker 8: and their goal is to incentivize fields where otherwise there 538 00:29:14,000 --> 00:29:18,000 Speaker 8: weren't sufficient finances to drive things. So they feel like 539 00:29:18,040 --> 00:29:22,600 Speaker 8: they're responsible for the current space exploration that's occurring in 540 00:29:22,640 --> 00:29:25,640 Speaker 8: the private sector because they sponsored an X Prize thirty 541 00:29:25,720 --> 00:29:29,040 Speaker 8: years ago that drove that market. So their goal with 542 00:29:29,120 --> 00:29:32,400 Speaker 8: this X Prize was to drive the same kind of 543 00:29:32,800 --> 00:29:37,080 Speaker 8: development and innovation in the area of biodiversity. So their 544 00:29:37,200 --> 00:29:40,440 Speaker 8: rules were that they would give us a few months 545 00:29:40,440 --> 00:29:44,560 Speaker 8: ahead of time, a random location in some rainforest on 546 00:29:44,600 --> 00:29:48,400 Speaker 8: the planet, give us one day to sample with only 547 00:29:48,960 --> 00:29:52,720 Speaker 8: drones and other kinds of remote sensing technologies. No human 548 00:29:52,800 --> 00:29:55,520 Speaker 8: was allowed to go into this square kilometer, and then 549 00:29:55,760 --> 00:29:58,640 Speaker 8: forty eight hours to analyze the data and provide a 550 00:29:58,720 --> 00:30:02,280 Speaker 8: report about the biodiversity that we encountered in that time. 551 00:30:03,080 --> 00:30:04,760 Speaker 7: What did you find here? Findings? 552 00:30:04,800 --> 00:30:06,360 Speaker 6: What was the bio I can't think of a more 553 00:30:06,400 --> 00:30:08,440 Speaker 6: biodiverse area maybe than a rainforest. 554 00:30:08,560 --> 00:30:13,080 Speaker 8: Well, we went to perhaps the most biodiverse place on Earth. 555 00:30:13,080 --> 00:30:17,440 Speaker 8: So this was a habitat in the Amazon rainforest. And 556 00:30:17,480 --> 00:30:20,840 Speaker 8: so we had a square kilometer just outside of Manaos 557 00:30:21,320 --> 00:30:24,640 Speaker 8: in Brazil, and so we deployed our drones and these 558 00:30:24,640 --> 00:30:28,120 Speaker 8: devices that sat on top of the rainforest canopy, and 559 00:30:28,160 --> 00:30:32,840 Speaker 8: they collected insects and sound and environmental DNA, and we 560 00:30:32,840 --> 00:30:37,000 Speaker 8: were able to take like twenty seven million samples of 561 00:30:37,280 --> 00:30:42,240 Speaker 8: genetic information from the forest, identified more species of birds 562 00:30:42,240 --> 00:30:44,280 Speaker 8: that exist in all of North America in this one 563 00:30:45,240 --> 00:30:49,280 Speaker 8: one kilometer area, and then measure hundreds of thousands of 564 00:30:49,880 --> 00:30:51,800 Speaker 8: insects all in this twenty four hour period. 565 00:30:51,800 --> 00:30:52,960 Speaker 7: It's really unprecedented. 566 00:30:53,000 --> 00:30:55,360 Speaker 2: So okay, so you take this, you analyze that you 567 00:30:55,360 --> 00:30:57,320 Speaker 2: have a tremendous amount of research. 568 00:30:57,160 --> 00:30:58,640 Speaker 7: Then what then what? 569 00:30:59,160 --> 00:31:02,480 Speaker 8: Well, that's the I think the big problem that Xprise 570 00:31:02,560 --> 00:31:05,680 Speaker 8: is trying to identify, which is first to develop the 571 00:31:05,720 --> 00:31:08,280 Speaker 8: technology so that we can do this kind of analysis 572 00:31:08,480 --> 00:31:10,480 Speaker 8: and then the next steps. The part that we're in 573 00:31:10,520 --> 00:31:12,840 Speaker 8: now is to try and develop and address the market 574 00:31:12,920 --> 00:31:18,520 Speaker 8: for biodiversity monitoring not only in rainforest and critically important 575 00:31:18,520 --> 00:31:21,600 Speaker 8: habitats like the Amazon Basin, but across the planet. 576 00:31:22,560 --> 00:31:26,120 Speaker 6: So what are the next technological frontiers for monitoring? 577 00:31:27,040 --> 00:31:32,080 Speaker 8: So we've now developed and tested and proven these technologies, 578 00:31:32,120 --> 00:31:35,560 Speaker 8: so our goal now is to translate these things into businesses. 579 00:31:36,120 --> 00:31:39,560 Speaker 8: So our team alone has generated six or seven new 580 00:31:39,600 --> 00:31:43,280 Speaker 8: businesses that are each focusing on components of this biodiversity 581 00:31:43,400 --> 00:31:46,760 Speaker 8: monitoring that are entering the market at this moment. And 582 00:31:46,800 --> 00:31:49,640 Speaker 8: the other teams that we compete it with, some of 583 00:31:49,680 --> 00:31:52,760 Speaker 8: their teams are also generating these new companies. New companies 584 00:31:52,760 --> 00:31:56,760 Speaker 8: that do things like monitoring environmental DNA at. 585 00:31:56,640 --> 00:31:57,720 Speaker 7: A particular location. 586 00:31:57,880 --> 00:32:01,720 Speaker 8: So if you're building a power plant somewhere along an 587 00:32:01,800 --> 00:32:04,480 Speaker 8: endangered forest, you want to know what your impacts are 588 00:32:04,560 --> 00:32:08,360 Speaker 8: you measured the environmental DNA to know what species were 589 00:32:08,360 --> 00:32:11,160 Speaker 8: there before and what species what your impact is on 590 00:32:11,240 --> 00:32:11,960 Speaker 8: species later. 591 00:32:12,560 --> 00:32:14,160 Speaker 3: It's great that we're having this on Earth Day, do 592 00:32:14,200 --> 00:32:14,360 Speaker 3: you know? 593 00:32:14,840 --> 00:32:18,520 Speaker 2: I know it's cool, but I was kind of joking, 594 00:32:18,560 --> 00:32:20,640 Speaker 2: not joking with some of my producers, being like. 595 00:32:20,560 --> 00:32:21,560 Speaker 3: Do we still care about that? 596 00:32:21,720 --> 00:32:24,600 Speaker 2: As in like, was this research much more relevant in 597 00:32:24,640 --> 00:32:28,080 Speaker 2: certain areas two years ago than you could make an argument. 598 00:32:27,760 --> 00:32:28,240 Speaker 9: That is now? 599 00:32:28,560 --> 00:32:32,000 Speaker 8: Well, I don't think so. I mean, in one sense, 600 00:32:32,120 --> 00:32:35,200 Speaker 8: Earth Day is the greatest disappointment ever right in that 601 00:32:36,280 --> 00:32:38,920 Speaker 8: and also kind of a weird thing to say. Every 602 00:32:39,040 --> 00:32:41,480 Speaker 8: day we live on Earth as far as I can tell, 603 00:32:41,640 --> 00:32:45,760 Speaker 8: and so what kind of action can we generate here? 604 00:32:45,840 --> 00:32:50,920 Speaker 8: So obviously the most important thing is to align market 605 00:32:50,960 --> 00:32:55,560 Speaker 8: interests along with saving and preserving biodiversity. And lots of 606 00:32:55,600 --> 00:33:02,280 Speaker 8: companies rely on services provided by nature, and so those 607 00:33:02,400 --> 00:33:05,800 Speaker 8: companies have already recognized that and already are engaged in 608 00:33:06,320 --> 00:33:10,240 Speaker 8: saving the habitats on which they rely on. A great 609 00:33:10,280 --> 00:33:13,240 Speaker 8: example is Laureal. This is a company that has a 610 00:33:13,280 --> 00:33:17,320 Speaker 8: global mission for making sure that the impacts of the 611 00:33:17,360 --> 00:33:22,440 Speaker 8: products they generate are going to be neutral over the 612 00:33:22,560 --> 00:33:26,000 Speaker 8: entire lifespan of the product from production to use and 613 00:33:26,080 --> 00:33:29,240 Speaker 8: then the discarding of the waste afterwards. 614 00:33:29,720 --> 00:33:32,320 Speaker 6: Do you sense changing winds out there in terms of funding, 615 00:33:33,000 --> 00:33:36,479 Speaker 6: terms of support for biodiversity and just environment in general. 616 00:33:36,760 --> 00:33:40,960 Speaker 8: Well, I mean it's complicated, of course, with changing political winds, 617 00:33:41,000 --> 00:33:44,080 Speaker 8: but we all live on this planet and that's not changing. 618 00:33:44,080 --> 00:33:46,120 Speaker 8: And I think anyone of our age and I don't 619 00:33:46,120 --> 00:33:49,320 Speaker 8: mean to say anything about how old any of us are, 620 00:33:49,360 --> 00:33:54,160 Speaker 8: but it's inescapable that during your lifetime you have observed 621 00:33:54,200 --> 00:34:00,600 Speaker 8: changes in climate and in biodiversity. That occurs, and whether 622 00:34:00,840 --> 00:34:03,320 Speaker 8: we like it or not, this is something that we're 623 00:34:03,360 --> 00:34:05,200 Speaker 8: going to have to deal with. The question I think 624 00:34:05,240 --> 00:34:07,680 Speaker 8: from a business perspective, of course, is what's the time 625 00:34:07,720 --> 00:34:10,480 Speaker 8: horizon of that? Is it one year, ten years, one 626 00:34:10,560 --> 00:34:13,359 Speaker 8: hundred years? And that's a complicated thing that I am 627 00:34:13,360 --> 00:34:14,560 Speaker 8: not equipped to answer. 628 00:34:15,560 --> 00:34:16,640 Speaker 3: What's next for you guys? 629 00:34:17,200 --> 00:34:21,319 Speaker 8: So I'm personally. I've started a company that came out 630 00:34:21,320 --> 00:34:25,799 Speaker 8: of this Xprize competition and so we have our first order, 631 00:34:25,840 --> 00:34:29,839 Speaker 8: and so I'm busy building things, building these high tech 632 00:34:29,880 --> 00:34:33,200 Speaker 8: devices that are deployable into these kinds of habitats that 633 00:34:33,239 --> 00:34:36,319 Speaker 8: collect this kind of data. And we see that is 634 00:34:36,600 --> 00:34:39,040 Speaker 8: at least on a small scale, a sustainable business for 635 00:34:39,120 --> 00:34:42,239 Speaker 8: quite quite some time. Anyone who owns land and is 636 00:34:42,280 --> 00:34:46,640 Speaker 8: interested in in the biodiversity there starting with like national 637 00:34:46,719 --> 00:34:50,920 Speaker 8: parks or local and city parks, or any other business 638 00:34:50,920 --> 00:34:53,839 Speaker 8: that have large landing holdings, they're going to need over 639 00:34:53,880 --> 00:34:59,520 Speaker 8: time devices like this to answer regulatory and their customers 640 00:34:59,560 --> 00:35:02,799 Speaker 8: demands about biodiversity. 641 00:35:02,520 --> 00:35:04,920 Speaker 7: And fascinating stuff. Eric, thank you so much for joining us. 642 00:35:05,000 --> 00:35:08,160 Speaker 6: Eric Fortune, here's a social professor of biological sciences here 643 00:35:08,200 --> 00:35:11,560 Speaker 6: at nj IT here in Newark, New Jerseys. 644 00:35:11,560 --> 00:35:13,799 Speaker 7: We appreciate getting a few minutes of his time. 645 00:35:15,520 --> 00:35:19,240 Speaker 1: You're listening to the Bloomberg Intelligence Podcast. Catch us live 646 00:35:19,320 --> 00:35:22,680 Speaker 1: weekdays at ten am Eastern on Applecarplay and Android Auto 647 00:35:22,800 --> 00:35:25,880 Speaker 1: with the Bloomberg Business app. Listen on demand wherever you 648 00:35:25,920 --> 00:35:28,880 Speaker 1: get your podcasts, or watch us live on YouTube. 649 00:35:29,880 --> 00:35:31,280 Speaker 7: All right, al steal Paul Sweeting. 650 00:35:31,320 --> 00:35:34,160 Speaker 6: We're live at the New Jersey Institute of Technology and 651 00:35:34,520 --> 00:35:38,960 Speaker 6: JIIT in Newark, New Jersey, talking to some really smart people. 652 00:35:40,080 --> 00:35:41,840 Speaker 7: What are you doing here? I mean this one we saved. 653 00:35:42,239 --> 00:35:46,880 Speaker 6: Somebody actually does this A neural engineer and brain stimulation scientist. 654 00:35:47,160 --> 00:35:49,520 Speaker 7: That is awesome. Put that on a business card. 655 00:35:50,080 --> 00:35:55,520 Speaker 6: Alisa Kalioniami, Assistant Professor Biomedical Engineering here at NJIT joins 656 00:35:55,600 --> 00:35:59,120 Speaker 6: us here. Alisa, what are you guys looking at? What's 657 00:35:59,160 --> 00:36:01,760 Speaker 6: your research you focusing on these days? 658 00:36:02,200 --> 00:36:05,640 Speaker 13: Yeah, so the biggest question my research is trying to 659 00:36:05,760 --> 00:36:11,600 Speaker 13: understand how to modulate the brain safely and precisely. So 660 00:36:11,920 --> 00:36:16,120 Speaker 13: we already know that several brain disorders have like abnormal 661 00:36:16,239 --> 00:36:21,520 Speaker 13: brain activities, but we don't know what causes them and 662 00:36:21,680 --> 00:36:25,080 Speaker 13: kind of like how can we normalize them? And that's 663 00:36:25,080 --> 00:36:28,000 Speaker 13: where brain stimulation comes from. So prain stimulation is a 664 00:36:28,040 --> 00:36:30,400 Speaker 13: method where we can actually modulate the brain safely. 665 00:36:30,600 --> 00:36:33,239 Speaker 2: Modulate the brain does that mean like fix it or 666 00:36:33,520 --> 00:36:35,359 Speaker 2: change the brain waves or what does that mean? 667 00:36:35,719 --> 00:36:38,359 Speaker 13: So basically it's kind of like the radio. So like 668 00:36:38,520 --> 00:36:41,200 Speaker 13: with the radio, you can find two things. So with 669 00:36:41,280 --> 00:36:45,960 Speaker 13: this one, we are applying these like small energy pulses 670 00:36:46,000 --> 00:36:49,320 Speaker 13: to the brain that are totally safe and these energy 671 00:36:49,360 --> 00:36:52,960 Speaker 13: pulses are able to change your brain activity. 672 00:36:54,440 --> 00:36:54,760 Speaker 7: Wow. 673 00:36:54,960 --> 00:36:58,719 Speaker 6: So give us like a typical example of kind of 674 00:36:58,760 --> 00:37:01,319 Speaker 6: what you're trying to do a patient who may have 675 00:37:01,320 --> 00:37:02,040 Speaker 6: some brain issues. 676 00:37:02,120 --> 00:37:02,880 Speaker 7: What's an example? 677 00:37:03,560 --> 00:37:09,080 Speaker 13: Yeah, So, well, for example, considering medications, So medications are 678 00:37:09,120 --> 00:37:13,359 Speaker 13: life saving for many individuals. But the challenge is that 679 00:37:13,400 --> 00:37:17,640 Speaker 13: like some people get side effects, some people don't just 680 00:37:18,080 --> 00:37:22,879 Speaker 13: like tolerate them. Some people just don't get like any response, 681 00:37:23,120 --> 00:37:25,040 Speaker 13: and obviously that's a problem because then we don't have 682 00:37:25,080 --> 00:37:27,640 Speaker 13: any treatments for those. So what I'm trying to do 683 00:37:27,680 --> 00:37:30,520 Speaker 13: with my research is kind of like help those individuals 684 00:37:30,520 --> 00:37:33,960 Speaker 13: who don't get help from the pharmaceuticals. So with these 685 00:37:33,960 --> 00:37:37,920 Speaker 13: brain simulation methods, we kind of like fill that gap 686 00:37:38,040 --> 00:37:41,240 Speaker 13: and try to help them. So we try to develop 687 00:37:41,320 --> 00:37:44,879 Speaker 13: methods that we could kind of like whatever problem they 688 00:37:44,920 --> 00:37:50,319 Speaker 13: have in their brain, we could elevate their symptoms, and 689 00:37:50,400 --> 00:37:52,920 Speaker 13: then in that case it's sort of customized per person 690 00:37:53,040 --> 00:37:53,480 Speaker 13: to do that. 691 00:37:53,560 --> 00:37:57,120 Speaker 3: So I mean, that's amazing. That's like a life saving thing. 692 00:37:57,160 --> 00:37:59,560 Speaker 3: You say it's totally safe, but you say electric magneta. 693 00:37:59,239 --> 00:38:01,160 Speaker 2: Pulsis in your brain, and you're like, WHOA, I don't know, 694 00:38:01,200 --> 00:38:01,960 Speaker 2: that sounds scary. 695 00:38:03,239 --> 00:38:04,600 Speaker 3: Give me the pitch for why it's safe. 696 00:38:05,520 --> 00:38:09,040 Speaker 13: So so basically with uh, this these path is we 697 00:38:09,080 --> 00:38:12,520 Speaker 13: can just reach the surface of the brain and then 698 00:38:13,120 --> 00:38:16,919 Speaker 13: like your brain is already naturally electrical, So what we're 699 00:38:16,960 --> 00:38:20,560 Speaker 13: basically doing is that we just like initiate the activity 700 00:38:20,600 --> 00:38:23,000 Speaker 13: that you would be initiating yourself as well, but we 701 00:38:23,120 --> 00:38:27,160 Speaker 13: just do it externally and then whatever was supposed to 702 00:38:27,200 --> 00:38:29,239 Speaker 13: happen in your brain will happen. So it's kind of 703 00:38:29,280 --> 00:38:33,000 Speaker 13: like we just initiate the domino effects, so to speak. 704 00:38:33,400 --> 00:38:36,520 Speaker 6: Where are you in your research now in terms of 705 00:38:36,560 --> 00:38:38,919 Speaker 6: maybe getting at some point two practical applications. 706 00:38:40,600 --> 00:38:43,360 Speaker 13: So my LAP is rather new, So I've been an 707 00:38:43,560 --> 00:38:46,520 Speaker 13: hit only like two and a half years, so I 708 00:38:46,560 --> 00:38:48,719 Speaker 13: would say that we're still at the kind of like 709 00:38:48,760 --> 00:38:53,920 Speaker 13: the first steps. But we already have some industry collaborations. 710 00:38:53,960 --> 00:38:57,400 Speaker 13: So we've worked with So there's a for example, this 711 00:38:57,480 --> 00:39:01,439 Speaker 13: program and SFI coores so that that's a program where 712 00:39:01,440 --> 00:39:04,120 Speaker 13: we collaborate with industry and then kind of like a 713 00:39:05,360 --> 00:39:08,760 Speaker 13: try to kind of like get an idea of where 714 00:39:08,800 --> 00:39:11,600 Speaker 13: we could help with our research. So I've had a 715 00:39:11,640 --> 00:39:15,480 Speaker 13: couple of student teams done that and then but basically, 716 00:39:15,520 --> 00:39:18,600 Speaker 13: like everything that we do, the end goal is to 717 00:39:18,680 --> 00:39:22,560 Speaker 13: help patients, so somehow, because I mean, this is electricity, 718 00:39:22,640 --> 00:39:25,560 Speaker 13: so obviously like that's where the engineering comes from. But 719 00:39:25,719 --> 00:39:28,319 Speaker 13: like in addition, obviously we have to understand other feels 720 00:39:28,400 --> 00:39:32,440 Speaker 13: like neuroscience and clinical things. But like from my labs, perspective. 721 00:39:32,520 --> 00:39:35,719 Speaker 13: You're trying to kind of like provide the engineering perspective, 722 00:39:35,840 --> 00:39:37,879 Speaker 13: So what do you need to do or what can 723 00:39:38,040 --> 00:39:41,719 Speaker 13: we do through an engineer's perspective to to model like 724 00:39:41,800 --> 00:39:44,360 Speaker 13: kind of like improve these methods so this. 725 00:39:44,440 --> 00:39:47,200 Speaker 3: Could become you could commercialize what you're doing. 726 00:39:47,560 --> 00:39:54,319 Speaker 13: So this technology is already commercialized. Okay, So basically this 727 00:39:54,480 --> 00:39:58,840 Speaker 13: was invented about thirty years ago. So there are several companies. 728 00:39:58,960 --> 00:40:02,000 Speaker 13: I believe currently there is like thirteen different companies that 729 00:40:02,040 --> 00:40:05,759 Speaker 13: are developing these these methods and there are FDA approved treatments. 730 00:40:05,760 --> 00:40:09,640 Speaker 13: So why we still need like a research is because 731 00:40:09,680 --> 00:40:12,480 Speaker 13: like we have this problem that like a we know 732 00:40:12,600 --> 00:40:16,239 Speaker 13: that this works, but we don't really understand the interaction 733 00:40:16,360 --> 00:40:20,040 Speaker 13: between the brain and the electricity that well. So okay, 734 00:40:20,080 --> 00:40:22,200 Speaker 13: we know that it works in this one individual, but 735 00:40:22,239 --> 00:40:24,800 Speaker 13: then like how do we modify to the second individual? 736 00:40:24,920 --> 00:40:25,920 Speaker 7: That's the mystery. 737 00:40:26,040 --> 00:40:28,719 Speaker 13: So we're trying to kind of like find find out 738 00:40:28,719 --> 00:40:30,799 Speaker 13: that what is the like what do we have to do, 739 00:40:30,920 --> 00:40:32,200 Speaker 13: like what do you have to change? 740 00:40:32,840 --> 00:40:34,080 Speaker 3: So currently is FDA. 741 00:40:33,880 --> 00:40:38,920 Speaker 13: Approved the things like depression OCDS, so obsessive compulsive disorder 742 00:40:39,080 --> 00:40:43,160 Speaker 13: and microants with ours, but everything's like one size with all. 743 00:40:43,800 --> 00:40:45,399 Speaker 9: So if you have like. 744 00:40:45,440 --> 00:40:49,640 Speaker 13: Let's say, like a your your tenetic somehow different, it 745 00:40:49,760 --> 00:40:52,840 Speaker 13: is like it might not work for you, but then. 746 00:40:52,800 --> 00:40:55,600 Speaker 3: Currently we don't really know why and what should we do? 747 00:40:56,480 --> 00:40:58,200 Speaker 7: Interesting? Are you saying that? Interesting? 748 00:40:58,239 --> 00:41:01,080 Speaker 6: That re research there for sure, Lisa, thank you so 749 00:41:01,160 --> 00:41:04,000 Speaker 6: much for joining us A Lisa Kelli and Niam Assistant 750 00:41:04,000 --> 00:41:08,920 Speaker 6: Professor Biomedical Engineering n JIT got some smart folks here 751 00:41:08,920 --> 00:41:10,759 Speaker 6: and we're glad they could spare a few minutes of 752 00:41:10,800 --> 00:41:11,800 Speaker 6: their time here today. 753 00:41:12,400 --> 00:41:17,080 Speaker 1: This is the Bloomberg Intelligence Podcast, available on Apple, Spotify, 754 00:41:17,280 --> 00:41:20,759 Speaker 1: and anywhere else you get your podcasts. Listen live each 755 00:41:20,760 --> 00:41:24,560 Speaker 1: weekday ten am to noon Eastern on Bloomberg dot com, 756 00:41:24,640 --> 00:41:28,200 Speaker 1: the iHeartRadio app, tune In, and the Bloomberg Business app. 757 00:41:28,600 --> 00:41:31,520 Speaker 1: You can also watch us live every weekday on YouTube 758 00:41:31,920 --> 00:41:34,160 Speaker 1: and always on the Bloomberg terminal